Content teams are rarely short of ideas. They are short of hours: the brief that never gets written properly, the article that sits half-finished in a shared folder, the webinar that never becomes a newsletter or a run of LinkedIn posts. Content creation automation connects the steps around the writing. Research notes become a structured content brief, the approved brief becomes a first draft from a large language model, and a finished piece is reshaped for your other channels. A named editor on your side approves every piece before anything is published or sent.
What this fixes, and what it does not
The aim is to remove the blank page and the copying and pasting, not the thinking. In most teams a writer spends a large part of each piece gathering the same inputs: the target query, notes from a sales call, product details, the house style, the internal pages worth linking to. A workflow can assemble those inputs every time, in the same order, so drafting starts from a full brief instead of a one-line request in a chat message.
What it will not do is decide what deserves publishing. Choosing topics, working out what your customers actually ask and giving each page one search intent is strategy work. If that is the real gap, start with content SEO and strategy before automating anything, because automating a weak plan only produces weak pages faster.
There is a search risk as well. Google’s spam policies treat pages produced in bulk mainly to rank, with little value for the reader, as scaled content abuse, whether a person or a model wrote them. Automation that skips the editor is exactly how a site ends up in that category.
Who this suits, and who it does not
It tends to suit:
- small in-house marketing teams producing content for a website, email and social channels every month;
- B2B firms whose specialists know the subject well but cannot spare the time to write it up;
- online shops writing and refreshing product and category copy in volume, where every description still needs checking against the product data;
- businesses that already publish regularly and want each piece to travel further, such as a long guide turned into an email feature and a set of social posts.
It is the wrong fit if you publish a handful of pieces a year, where a good freelance writer will serve you better, or if nobody can set aside regular time to edit. I will not build a workflow that generates hundreds of near-identical pages: they rarely survive Google’s spam systems and they give readers nothing.
What gets automated
Briefs built from your research
A short form or a row in a shared sheet starts the workflow: topic, target query, audience and any notes. The workflow pulls in material you already hold, such as the relevant product page, past articles on the subject and your style guide, and returns a brief with proposed headings, the questions to answer, internal links to include and the call to action. The writer edits the brief before any drafting starts. My SEO content brief template shows the fields I work from.
First drafts that follow your voice
The drafting step works only from the approved brief, your tone of voice notes and two or three examples of pieces your team rates highly. The output is a working draft for an editor, labelled as such, with visible gaps wherever it needs a fact, a figure or a quote that only your team can supply. I would rather the model leave a gap than guess, because a confident guess is how AI hallucinations slip into published copy.
Repurposing approved pieces
Once a piece is signed off, the workflow can produce derivatives from it: a newsletter section, LinkedIn post drafts, a short summary for the sales team, alternative headlines and meta descriptions. Each one returns to the editor as a draft. Scheduling and posting those drafts is a separate job, and turning video or podcast recordings into clips and show notes is a separate service again.
Checks before an editor sees it
Simple automatic checks catch the obvious problems early: banned phrases, American spellings in British copy, missing internal links, skipped heading levels and long sentences. My free readability and keyword density checker shows the kind of thing that can be tested this way. The checks flag issues for the editor; they never rewrite on their own.
What I deliberately keep human
Some steps stay with a person however capable the models become:
- Final approval of anything published or sent, with the editor’s name recorded against each piece.
- Facts, figures, prices and product claims. Under the CAP Code, objective claims in UK marketing need evidence held before publication, and a model has no idea what your business can substantiate.
- Expert opinion, customer stories and first-hand experience, which are what make content worth reading and what Google’s people-first guidance asks for.
- Regulated wording in sectors such as financial services, healthcare and legal services, which stays with whoever signs off compliance today.
This is the human in the loop principle applied to content: the automation prepares and a person decides. I build the approval into the workflow itself, so a draft cannot reach the publishing queue without it.
How I run a build
- Map one real piece. We trace how a recent article moved from idea to publication: who did what, where the inputs lived and where it stalled.
- Agree the rules. Which content types are in scope, what the editor must check, and what information may and may not be sent to an AI provider.
- Build a small version. One content type and one channel, end to end, using tools you already pay for where they can do the job.
- Test on live briefs. Your editor runs several real pieces through it and marks what the drafts got wrong. I adjust the prompts, inputs and checks until the drafts are consistently worth editing.
- Extend and hand over. Further content types and channels are added only once the first one works, and every step is documented so your team can change it without me.
Data protection and confidential material
Briefs often contain customer details, sales notes or unreleased product plans. Before anything is sent to an AI provider, I check where the provider processes data, whether your inputs can be used to train its models and whether a data processing agreement is in place, so the setup fits your UK GDPR obligations. Personal data is stripped from inputs unless there is a clear reason and a lawful basis to include it.
Problems I see most often
- Prompts that live in one person’s head or chat history, so quality drops whenever that person is away.
- Drafts generated without a brief, which come back generic because the model had nothing specific to work from.
- No record of who approved what, which makes an error hard to trace and fix later.
- Repurposed posts that repeat the article word for word on every channel instead of adapting it to how people use that channel.
- Teams counting output rather than asking whether the content brings in enquiries.
If you are weighing up AI for content more generally, my article on using AI for marketing without hurting SEO covers the search side in more depth.
What you receive
- The working workflow, built in accounts your business owns.
- A written library of prompts and inputs, with a note on what each one is for.
- An editor’s checklist and a recorded approval step for every piece.
- A plain list of what information is sent to which AI provider, and what is never sent.
- Documentation explaining each step and how to change it.
What it costs
The cost depends on how many content types and channels are in scope and which tools you already use. Where possible, model usage is billed by the AI provider to your own account, so you see that cost directly. Each quote is written after a free first call, in GBP, and agreed in writing before work starts.
Next step
Send me one recent piece of content and a rough note of how it was produced. On a free 30-minute call I will tell you which steps are worth automating, which should stay manual, and whether your volume justifies a build at all. You can book the call through my contact page.